mirror of
https://github.com/guezoloic/millesima_projetS6.git
synced 2026-04-02 15:21:34 +00:00
fix(learning.ipynb): cross_val_score erreur ajout X_train
This commit is contained in:
@@ -2,7 +2,7 @@
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": 31,
|
||||
"id": "faafb9a0",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -110,7 +110,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"execution_count": 32,
|
||||
"id": "8342340f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -135,7 +135,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"execution_count": 33,
|
||||
"id": "9dfdc01f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -184,7 +184,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"execution_count": 34,
|
||||
"id": "99de3ed7",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -217,7 +217,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": 35,
|
||||
"id": "09eca16d",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -255,7 +255,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": 36,
|
||||
"id": "b94a89f2",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -332,7 +332,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"execution_count": 37,
|
||||
"id": "4f1c169f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -369,7 +369,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"execution_count": 38,
|
||||
"id": "91cedffb",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -466,7 +466,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"execution_count": 39,
|
||||
"id": "4c21cd56",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -482,9 +482,9 @@
|
||||
"text/markdown": [
|
||||
"| Méthode | R² |\n",
|
||||
"| :---: | :---: |\n",
|
||||
"|AD|0.386319|\n",
|
||||
"|Normalisation + AD|0.386319|\n",
|
||||
"|Standardisation + AD|0.386319|\n"
|
||||
"|AD|0.483688|\n",
|
||||
"|Normalisation + AD|0.483688|\n",
|
||||
"|Standardisation + AD|0.483719|\n"
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.Markdown object>"
|
||||
@@ -505,9 +505,9 @@
|
||||
"text/markdown": [
|
||||
"| Méthode | R² |\n",
|
||||
"| :---: | :---: |\n",
|
||||
"|AD|0.382689|\n",
|
||||
"|Normalisation + AD|0.382695|\n",
|
||||
"|Standardisation + AD|0.382691|\n"
|
||||
"|AD|0.501187|\n",
|
||||
"|Normalisation + AD|0.501187|\n",
|
||||
"|Standardisation + AD|0.501185|\n"
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.Markdown object>"
|
||||
@@ -528,9 +528,9 @@
|
||||
"text/markdown": [
|
||||
"| Méthode | R² |\n",
|
||||
"| :---: | :---: |\n",
|
||||
"|AD|0.371764|\n",
|
||||
"|Normalisation + AD|0.374744|\n",
|
||||
"|Standardisation + AD|0.371729|\n"
|
||||
"|AD|0.511089|\n",
|
||||
"|Normalisation + AD|0.508518|\n",
|
||||
"|Standardisation + AD|0.512649|\n"
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.Markdown object>"
|
||||
@@ -543,7 +543,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"best score= 0.3863187373819251 depth= 3 method= AD\n"
|
||||
"best score= 0.512649496806262 depth= 5 method= Standardisation + AD\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -571,7 +571,7 @@
|
||||
" else make_pipeline(scaler(), DecisionTreeRegressor(max_depth=depth))\n",
|
||||
" )\n",
|
||||
" model.fit(X_train, y_train)\n",
|
||||
" score: float = cross_val_score(model, X_test, y_test, cv=5).mean()\n",
|
||||
" score: float = cross_val_score(model, X_train, y_train, cv=5).mean()\n",
|
||||
" ad_table.ajoutligne(f\"{name}\", score)\n",
|
||||
"\n",
|
||||
" if score > best_score_ad:\n",
|
||||
@@ -625,7 +625,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"execution_count": 40,
|
||||
"id": "f72f499f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -641,9 +641,9 @@
|
||||
"text/markdown": [
|
||||
"| Méthode | R² |\n",
|
||||
"| :---: | :---: |\n",
|
||||
"|KNN|0.370389|\n",
|
||||
"|Normalisation + KNN|0.341947|\n",
|
||||
"|Standardisation + KNN|0.369662|\n"
|
||||
"|KNN|0.485764|\n",
|
||||
"|Normalisation + KNN|0.497729|\n",
|
||||
"|Standardisation + KNN|0.489906|\n"
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.Markdown object>"
|
||||
@@ -664,9 +664,9 @@
|
||||
"text/markdown": [
|
||||
"| Méthode | R² |\n",
|
||||
"| :---: | :---: |\n",
|
||||
"|KNN|0.390801|\n",
|
||||
"|Normalisation + KNN|0.349482|\n",
|
||||
"|Standardisation + KNN|0.381631|\n"
|
||||
"|KNN|0.504888|\n",
|
||||
"|Normalisation + KNN|0.500504|\n",
|
||||
"|Standardisation + KNN|0.493475|\n"
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.Markdown object>"
|
||||
@@ -679,7 +679,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"best score= 0.39080066451618123 neighbor= 5 method= KNN\n"
|
||||
"best score= 0.5048884037669835 neighbor= 5 method= KNN\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -707,10 +707,10 @@
|
||||
" else make_pipeline(scaler(), KNeighborsRegressor(n_neighbors=n))\n",
|
||||
" )\n",
|
||||
" model.fit(X_train, y_train)\n",
|
||||
" score: float = cross_val_score(model, X_test, y_test, cv=5).mean()\n",
|
||||
" score: float = cross_val_score(model, X_train, y_train, cv=5).mean()\n",
|
||||
" knn_table.ajoutligne(f\"{name}\", score)\n",
|
||||
"\n",
|
||||
" if score > best_score_ad:\n",
|
||||
" if score > best_score_knn:\n",
|
||||
" best_score_knn = score\n",
|
||||
" best_neighbor = n\n",
|
||||
" best_scaler_name = name\n",
|
||||
@@ -749,7 +749,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"execution_count": 41,
|
||||
"id": "9f764f3a",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -759,8 +759,8 @@
|
||||
"| Méthode | R² |\n",
|
||||
"| :---: | :---: |\n",
|
||||
"|LR|0.452908|\n",
|
||||
"|AD|0.386319|\n",
|
||||
"|KNN|0.390801|\n"
|
||||
"|AD|0.512649|\n",
|
||||
"|KNN|0.504888|\n"
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.Markdown object>"
|
||||
@@ -773,7 +773,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"best_model= LR best_scaler= MinMaxScaler\n"
|
||||
"best_model= AD best_scaler= StandardScaler\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -813,7 +813,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"execution_count": 42,
|
||||
"id": "9084e87e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -850,17 +850,17 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"execution_count": 43,
|
||||
"id": "fdcdfb17",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0.24194691318111572"
|
||||
"0.3938363073944231"
|
||||
]
|
||||
},
|
||||
"execution_count": 24,
|
||||
"execution_count": 43,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -902,7 +902,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"execution_count": 44,
|
||||
"id": "c4f6c27f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -912,7 +912,7 @@
|
||||
"<Axes: >"
|
||||
]
|
||||
},
|
||||
"execution_count": 25,
|
||||
"execution_count": 44,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
@@ -947,7 +947,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"execution_count": 45,
|
||||
"id": "2e512052",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -1134,7 +1134,7 @@
|
||||
"App_Haut-Médoc -0.070383"
|
||||
]
|
||||
},
|
||||
"execution_count": 26,
|
||||
"execution_count": 45,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -1155,17 +1155,17 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"execution_count": 46,
|
||||
"id": "4010aa12",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0.24551258031003886"
|
||||
"0.4489472637581393"
|
||||
]
|
||||
},
|
||||
"execution_count": 27,
|
||||
"execution_count": 46,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -1213,7 +1213,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"execution_count": 47,
|
||||
"id": "1510f763",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -1222,9 +1222,9 @@
|
||||
"text/markdown": [
|
||||
"| Méthode | R² |\n",
|
||||
"| :---: | :---: |\n",
|
||||
"|Random Forest|0.492498|\n",
|
||||
"|Normalisation + RF|0.492721|\n",
|
||||
"|Standardisation + RF|0.500235|\n"
|
||||
"|Random Forest|0.496806|\n",
|
||||
"|Normalisation + RF|0.494283|\n",
|
||||
"|Standardisation + RF|0.486890|\n"
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.Markdown object>"
|
||||
|
||||
Reference in New Issue
Block a user